Deep learning predicts future visual field changes with high accuracy.
problem Forecasting future visual field changes in glaucoma patients.
method Ten-fold cross validation with transfer learning, using CascadeNet-5 model.
result Deep learning models can predict future visual field changes up to 5.5 years with high correlation and accuracy.
Generative adversarial networks improve spectral image visualization.
problem Displaying spectral images in natural colors for better interpretation.
method Proposes a GAN with adversarial and structure losses.
result Generative adversarial networks generate structure-preserved and natural-looking visualizations.
Study improves glaucoma prediction accuracy by aggregating clustering-based models.
problem Predicting glaucomatous visual field loss from limited patient data.
method Hierarchically aggregating clustering-based predictors to enhance prediction accuracy.
result Hierarchical aggregation of cluster-based predictors outperforms single methods.
This paper explores neural network loss landscapes and their effects on generalization.
problem Understanding the structure of neural network loss functions and their impact on generalization.
method Simple filter normalization and various visualization methods to explore loss landscape structure and network architecture effects.
result Visualizations reveal how network architecture and training parameters affect loss landscape curvature and minimizers.
New visualization techniques reveal GAN optimization landscapes.
problem Challenges in training GANs compared to standard deep neural networks.
method Visualization techniques for optimization landscapes of GANs.
result GANs converge to saddle points, not minima, achieving excellent performance.
Paper proposes a new black-box attack approach to minimize visual distortion.
problem Constructing adversarial examples that minimize visual distortion in a black-box threat model.
method Learning the noise distribution of adversarial examples to approximate the gradient of a non-differentiable loss function.
result The proposed attack results in much lower visual distortion compared to state-of-the-art black-box attacks.
Study visualizes actor-critic loss landscapes for inventory optimization.
problem Difficulties in solving multi-store dynamic inventory control problems.
method Low-dimensional visualizations of actor loss function.
result Loss landscapes favor optimal policies in reinforcement learning.
Visualizes robustness of adversarial malware models.
problem Interpreting robustness of adversarial malware models.
method Comparing loss behavior of hardened models with adversarial variants generated during training and other sources, using self-organizing maps.
result Generalization observed in naturally trained models extends to adversarially hardened models.
Efficiently visualizes uncertainty in local divergence of 2D vector fields.
problem Uncertainty in vector field data leads to inaccurate divergence computations.
method Closed-form approach for highly efficient and accurate uncertainty visualization of local divergence, assuming independently Gaussian-distributed vector uncertainties.
result Significantly enhanced efficiency and accuracy of our algorithms over classical MC approach.
A framework visualizes embedding spaces of neural survival analysis models using anchor directions.
problem Visualizing complex embeddings in neural survival analysis models.
method Estimating anchor directions through clustering or user-supplied concepts, revealing relationships with raw inputs and survival times.
result Visualization strategies reveal how anchor directions relate to raw clinical features and survival time distributions.
Researchers improve visualization of neural network loss landscapes.
problem Understanding neural network generalization performance.
method Novel 'jump and retrain' procedure, non-linear dimensionality reduction (PHATE), computational homology.
result Improved visualization and quantification of neural network generalization performance.
t-SNE loses important features in data visualization.
problem t-SNE's loss of important features in data visualization.
method Established mathematical framework to understand t-SNE's loss in different scenarios.
result t-SNE loses important features of data in various scenarios.
This work uses visualizations to make generalization of neural networks more intuitive.
problem Understanding the reasons behind neural networks' ability to generalize to unseen data.
method Visualization methods to explain the geometry of loss landscapes and the role of dimensionality in optimization.
result Visualization helps in understanding how optimizers settle into minima that generalize well.
New report on machine learning visualization techniques and trends.
problem Improving trust in machine learning models through visualization.
method Analysis of peer-reviewed articles on machine learning visualization techniques.
result Rapid growth in machine learning visualization techniques over the past three years.
New findings suggest adversarial training does not flatten loss landscapes, challenging current intuition.
problem Understanding and improving generalization in deep learning.
method Loss surface visualization with filter normalization technique.
result Adversarial training does not result in flatter loss landscapes, challenging current intuition.
Approach to develop visual perception in robots through sensorimotor interactions.
problem Developing autonomous perception in robots.
method Sensorimotor contingencies theory applied to robot exploration and learning.
result Captured sensorimotor regularities in a predictive model for visual field discovery.
Method generates visual explanations for similarity models without classification.
problem Lack of visual explanations for similarity models trained without classification loss.
method Gradient-based visual attention using learned feature embeddings.
result Attention maps improve model performance and can be used as constraints.
The Giroux correspondence and the notion of a near force-free magnetic field are used to topologically characterize near force-free magnetic fields which describe a variety of physical processes, including plasma equilibrium. As a byproduct, the topological characterization of force-free magnetic fields associated with…
Introduces CHL, a new loss function for continuous similarity learning.
problem Binary similarity learning limitations.
method CHL is a novel loss function that generalizes histogram loss to continuous similarities.
result CHL solves a wider range of tasks including similarity learning, representation learning, and data visualization.
Improved zero-shot learning with graph-based regularization.
problem Transfer knowledge to unknown classes in zero-shot learning.
method Isoperimetric loss for learning map between visual and semantic embeddings, exploiting graph structure.
result Regularization alone outperforms state-of-the-art methods in zero-shot learning benchmarks.
Study improves recognition of long-tail visual relationships.
problem Improving recognition of structured visual relationships from long-tail classes.
method Developed two benchmarks, introduced VilHub loss, and applied RelMix augmentation.
result Simple techniques significantly improved performance on tail classes.
The paper proposes a method to infer user profiles from multiple sources of social media data.
problem Mining user profiles from social media data using a single type of information.
method Hinge-loss Markov Random Fields (HL-MRFs) integrated with multiple sources of UGC and social relations.
result HL-MRFs successfully incorporate multiple sources of information and outperform competing methods.
GraphTSNE visualizes graph data by integrating graph structure and node features.
problem Lack of suitable visualization techniques for graph-structured data.
method GraphTSNE combines t-SNE with graph convolutional networks to visualize graph data.
result GraphTSNE produces better visualizations of graph data compared to existing methods.
GradVis visualizes and analyzes deep neural network optimization surfaces efficiently.
problem Understanding and optimizing deep neural network training landscapes.
method Developed an open-source library GradVis for efficient visualization and analysis of optimization surfaces.
result GradVis enables plotting of optimization surfaces and trajectories for large networks.
BEGAN improves GANs by balancing generator and discriminator, achieving high visual quality.
problem Training GANs to achieve high visual quality and balance diversity and quality.
method Proposes a new equilibrium enforcing method with a Wasserstein distance loss, providing a convergence measure and controlling trade-offs.
result Achieves high visual quality in image generation tasks, even at higher resolutions, using a simple model architecture.
Study evaluates interpretability of time series foundation models' latent spaces.
problem Improving interpretability of latent spaces in time series models for visual analytics.
method Evaluated MOMENT family of transformer-based models on five datasets, fine-tuning for performance.
result Fine-tuning improved latent space clarity but limited interpretability remained.
This research evaluates neural network robustness through loss visualization and a new metric.
problem Neural networks' robustness property is insufficiently investigated compared to adversarial attacks and defenses.
method Loss visualization and a new robustness metric to evaluate model stability.
result The proposed robustness metric provides a more reliable evaluation of model stability, uniformed across different models and settings.
IANN visualizes all input variables effects simultaneously.
problem Inability to visualize all input variables effects simultaneously in black-box functions.
method Interpretable Architecture Neural Network (IANN) approach.
result Visualization of all input variables effects directly and simultaneously.
UN-AVOIDS visualizes and detects anomalies without needing labeled data.
problem The need for a unified framework to visualize and detect anomalies.
method UN-AVOIDS is an unsupervised, nonparametric approach that transforms data into a new space (NCDF) for both visualization and detection.
result UN-AVOIDS assigns invariant anomalous scores and achieves high AUC in detecting anomalies.
Boosts neural network performance by improving weight separability.
problem Improving the separability of weight vectors in neural networks.
method Proposes a new evaluation metric and feed-backward reconstruction loss to encourage weight separability.
result Improves visual recognition performance across various tasks.
New method prevents class collapse in metric learning with margin-based losses.
problem Class collapse in metric learning due to diverse intra-class samples.
method Proposed a sampling method to select nearest same-class samples as positive elements in tuple.
result Demonstrated clear benefits on various fine-grained image retrieval datasets.
Prototype-based memory network learns visual categories from unlabeled data.
problem Learning from nonstationary, unlabeled data with sequential dependencies.
method Online prototype-based memory network with contrastive loss.
result Significantly better category recognition compared to state-of-the-art methods.
DEN creates interpretable visualizations using Siamese networks.
problem Creating interpretable visualizations of complex datasets.
method Differentiating Embedding Networks (DEN) using Siamese neural networks and loss functions.
result DEN outperforms existing techniques on FashionMNIST and interpretable features are identified.
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
problem Mitigating energy consumption in commercial buildings through occupant plugload control.
method Field experiments with visual feedback and monetary incentives in government and university buildings.
result Mean energy reduction of ~9.52% in office environments and ~21.61% in university environments with visual feedback.
AVH scores measure sample hardness, improving model calibration.
problem CNNs' poor calibration and overconfidence issues.
method AVH score based on normalized angular distance between feature embeddings and target classifiers.
result AVH scores correlate with human visual hardness and improve model calibration.
V-CNN improves CNN performance in network intrusion detection.
problem Applying CNN directly to non-image data leads to poor performance.
method Integrates data visualization before CNN modeling.
result Significantly outperforms other studies in network intrusion detection.
BYOL-Explore learns to explore visually-rich environments by predicting world dynamics.
problem Exploration in visually complex environments.
method Optimizes a single prediction loss in latent space to learn world representation, dynamics, and exploration policy.
result Achieves superhuman performance on Atari games with simpler design.
UMAP's true loss function differs from what was previously thought, focusing on nearest neighbor graph similarities.
problem Understanding the true effectiveness and mechanism of UMAP for high-dimensional data visualization.
method Deriving UMAP's effective loss function in closed form and analyzing its optimization scheme.
result UMAP aims to reproduce similarities encoded in the nearest neighbor graph, not the full high-dimensional similarities.
Deep reinforcement learning enhances AI systems' visual understanding.
problem Scaling reinforcement learning to complex visual tasks.
method Value-based and policy-based methods, deep neural networks.
result Deep reinforcement learning enables autonomous systems to learn from raw visual inputs.
This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any orga…
Recent studies in the field of human vision science suggest that the human responses to the stimuli on a visual display are non-deterministic. People may attend to different locations on the same visual input at the same time. Based on this knowledge, we propose a new stochastic model of visual attention by introducing…
Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations. However, solving inverse problems with sparsifying dictionaries can be computationally expensive, especially when the dictionary under consideration has a …
SPL-ADVisE improves deep learning convergence and accuracy.
problem Training deep neural networks with self-paced learning and adaptive embeddings.
method Integrates self-paced learning and deep metric learning using Magnet Loss for dynamic mini-batch selection.
result SPL-ADVisE converges faster and achieves higher accuracy on fine-grained datasets.
CNNs reveal retinal ganglion cell features, linking visual processing to neuroscience.
problem Understanding what CNNs learn about retinal neuronal circuits.
method Trained CNNs on white noise images to predict neural responses from salamander retinas.
result CNN filters resemble biological retinal components and ganglion cell receptive fields.
The aim of this paper is to present a new method for visual place recognition. Our system combines global image characterization and visual words, which allows to use efficient Bayesian filtering methods to integrate several images. More precisely, we extend the classical HMM model with techniques inspired by the field…
M-PHATE visualizes neural network learning dynamics.
problem Understanding neural network performance and learning dynamics.
method Multislice PHATE (M-PHATE) for visualizing neural network hidden representations.
result M-PHATE provides detailed summaries of learning dynamics without needing validation data.
Paper introduces Balanced Meta-Softmax for better long-tailed visual recognition.
problem Long-tailed distribution mismatch between training and testing data.
method Balanced Meta-Softmax, an unbiased extension of Softmax, using a Meta Sampler.
result Balanced Meta-Softmax outperforms state-of-the-art solutions on visual recognition and instance segmentation.
Visualizes DNNs using topographic maps for better understanding.
problem Difficulty in understanding how DNNs solve tasks.
method Adapting neuroscience methods to visualize DNN activations.
result Improved transparency and interpretability of DNN-based systems.